from_pretrained("bert-base-chinese") model(**tokenizer("弱小的我也有大梦想", return_tensors="pt")) 这里用到了Tokenizer的一个额外参数return_tensors,指定该参数的值,Tokenizer进行数据处理后将返回对应框架格式的数据,如pt对应pytorch,tf对应TensorFlow。 不同NLP任务的对应模型 前面介绍了如何加载基础的预...
This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations. conversationsemotioninferencedatasetcausalitynatural-language-inferencecausal-inferencedialogue-systemsreasoningemotion-recognitioncausal-modelsdialogue-generationrobertabert-modelem...
Causal Language Modeling (CausalLM):Suitable for tasks requiring sequential data generation. Masked Language Modeling (MLM):Ideal for tasks needing bidirectional context understanding. Sequence-to-Sequence (Seq2Seq):Used for tasks like translation or summarization where input sequences are mapped to outp...
We make several optimizations to improve the train-ing speed of our models. First, we use an efficient implementation of the causal multi-head attention to reduce memory usage and runtime. This imple-mentation, available in the xformers library,2 is inspired by Rabe and Staats (2021) and u...
Validate the outputs of the PyTorch and exported models. In this section, we'll look at how DistilBERT was implemented to show what's involved with each step. Implementing a custom Core ML configuration TODO: didn't write this section yet because the implementation is not done yet ...
为了解决这个问题,我们很高兴地宣布 Keras 生态系统迎来重大变革: 隆重推出KerasHub,一个统一、全面的预训练模型库,简化了对前沿 NLP 和 CV 架构的访问。KerasHub 是一个中央存储库,您可以在稳定且熟悉的 Keras 框架内无缝探索和使用最先进的模型,例如用于文本分析的 BERT 以及用于图像分类的 EfficientNet。
# lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(self.context_length, self.context_length) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return ma...
A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reas
We invite you to be part of this exciting journey. Whether you're looking to integrate STT technology into your business, develop new applications, or simply stay informed about the latest advancements, there are numerous resources and APIs available to get started. Embrace the potential of AI-...
Model namedistilbert-base-uncased-finetuned-sst-2-englishon top. Framework (the red box is ours):transformers Task (the blue box is ours):Text Classification Supported underlying frameworks: PyTorch + TensorFlow Files and Versions tab, which contains model file sizes etc. ...